The present invention relates to a monitoring apparatus using an image pickup device and particularly to an intruding object detecting method and an intruding object monitoring apparatus for automatically detecting an object intruding into a monitoring visual field, as a target object to be detected, from video signals supplied from an image pickup device under a monitoring environment in which the trembling of trees, waves or the like is also observed.
An intruding object monitoring apparatus using an image pickup device such as a camera as an image input means is to detect an object intruding into a monitoring visual field or to confirm the kind of the object to thereby automatically issue a predetermined announcement or alarm without depending on manned monitoring by a watcher which is hetherto done. In order to achieve such a system, there is a method in which: an input image obtained from the image input means such as a camera is first compared with a reference background image (that is, an image in which an object to be detected is not picked up) or with another input image which was obtained at a time different from the time when the first-mentioned input image is obtained; a difference between the input image and the reference background image or between the two input images is detected for each pixel; and a region having a large difference is extracted as an object. This method is known as “subtraction method” and has been widely used conventionally. Particularly, the method using the difference between the input image and the reference background image is known as “background subtraction method” and the method using the difference between the input images obtained at different times is known as “frame subtraction method”.
The processing by the background subtraction method will be first described with reference to FIG. 5. FIG. 5 is a diagram for explaining the principle of processing the object detection according to the background subtraction method. In FIG. 5, a reference numeral 101 designates an input image; 105, a reference background image; 501, a difference image according to the background subtraction method; 502, a binarized image of the difference image 501; 112, a subtractor; and 115, a binarizer.
In FIG. 5, the subtractor 112 calculates the difference in luminance value between two frame images (that is, the input image 101 and the reference background image 105 in FIG. 5) for each pixel to thereby output the difference image 501. The binarizer 115 produces the binarized image 502 in the condition that the pixel value of each pixel of the difference image 501 is set to “0” when it is smaller than a predetermined threshold value Th and the pixel value is set to “255” when it is equal to or greater than the threshold value Th (the pixel value of one pixel is calculated on the assumption that each pixel is composed of 8 bits).
The human-like object 503 picked up in the input image 101 in this manner is calculated as a region 504 where a difference is generated by the subtractor 112. The region 504 is then detected by the binarizer 115 as an image 505 indicating a cluster of pixels with the pixel value of “255”. For example, JP-A-9-288732 discloses an application example of the background subtraction method.
Next, the processing by the frame subtraction method will be described with reference to FIG. 6. FIG. 6 is a diagram for explaining the principle of processing the object detection according to the frame subtraction method. In FIG. 6, a reference numeral 101 designates a first input image; 102, a second input image which is obtained by imaging the same range of visual field as the first input image at a time different from the time when the first input image 101 is obtained; 601, a difference image according to the frame subtraction method; 602, a binarized image of the difference image 601; 112, a subtractor; and 115, a binarizer.
In FIG. 6, the subtractor 112 calculates the difference in luminance value between two frame images (that is, the first input image 101 and the second input image 102 in FIG. 6) for each pixel and outputs the difference image 601 in the same manner as that in FIG. 5. The binarizer 115 produces the binarized image 602 in the condition that the pixel value of each pixel of the difference image 601 is set to “0” when it is smaller than a predetermined threshold value Th and the pixel value is set to “255” when it is equal to or greater than the threshold value Th (the pixel value of one pixel is calculated on the assumption that each pixel is composed of 8 bits) in the same manner as that in FIG. 5.
The human-like objects 603 and 604 picked up in the first and second input images 101 and 102 respectively in this manner are calculated as a region 605 where a difference is generated by the subtractor 112. The region 605 is detected by the binarizer 115 as an image 606 indicating a cluster of pixels with the pixel value of “255”. For example, JP-B-2633694 discloses an application example of the frame subtraction method.